1a117652fd
* Refresh expired Kimi usage tokens * Test Kimi usage token refresh * Harden Kimi usage token refresh Follow-up review of the refresh path added in f6ce001a. Credential reads no longer depend on the shape of fields this provider only writes back: declaring expires_at and friends as required numbers meant a null or stringified value threw away the access token sitting next to them, turning a working Kimi quota bar into "unavailable" with no way back. Those fields are nullish and coerced now. Refreshed tokens are merged into a re-read of the credential file rather than the copy the request started with, so a write by Kimi Code during the refresh round-trip survives, and a file deleted in that window stays deleted instead of being resurrected with fresh credentials. A failed write logs at warn: Kimi rotates the refresh token, so losing that write silently logs the user out of Kimi Code. Only 401 triggers a refresh. A 403 means authenticated but not entitled, which no refresh fixes, and refreshing anyway rotated the user's refresh token on every poll. Also: floor expires_at to whole seconds, indent the file the way Kimi Code does, derive the temp file name from the real basename, and split out fetchUsageResponse, which is what puts fetchUsage back under the complexity limit — the original passed neither lint nor the formatter. * Keep CLI-rotated Kimi credentials during refresh The Kimi CLI shares the credential file the quota fetcher writes. A refresh read the file, merged its new tokens, and renamed over it, so a rotation that landed while the refresh was in flight was replaced with stale tokens and the CLI's session broke until credentials were recreated. Thread the refresh token that was actually spent into the persistence step and skip the write when the file no longer carries it. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> * fix(kimi): normalize additional usage windows * fix(kimi): preserve custom auth while parsing limit windows * test(kimi): cover additional usage windows * test(kimi): move usage-window tests into service.test.ts Every other quota provider is tested in service.test.ts; kimi.test.ts was the only provider with a standalone collocated test file. * fix(kimi): resolve reasoning-effort options per model, not per catalog probe deriveModelDefinitionsFromACP derived thinkingOptions once from whichever model happened to be active in the one-off catalog probe session, then copied that same array onto every model. Kimi's ACP adapter only reports thinking levels for the currently selected model, so K2.7/K2.7-coding-fast (boolean on/off) and K3 (off/low/medium/high) ended up with identical, often-wrong options depending on which model the probe defaulted to. fetchCatalog now reuses the single probe session to switch through each candidate model and read back its real thought_level options, falling back to the default options if an individual model's probe fails. Skips entirely when there's one model or no thinking picker, so providers without per-model effort levels pay no extra round trips. * refactor(kimi): merge kimi-usage.ts back into kimi.ts kimi.ts was the only quota provider split across two files; every other provider (including claude.ts at 503 lines, larger than the merged Kimi file) stays in one. Also fixes a nested-ternary lint error surfaced by the merge. * fix(kimi): re-validate credentials immediately before the rename Greptile flagged that saveRefreshedCredentials read the credential file, checked its refresh token against a stale snapshot, then did async writeFile work before an unconditional rename — leaving a window where the Kimi CLI could rotate the file after the check and have its newer tokens clobbered by our stale merge. The refresh-token-only comparison also missed a CLI access-token rotation that kept the same refresh token. Re-read the file immediately before the rename and compare both tokens against the snapshot the merge was built from, discarding the merge if anything changed. Narrows the check-then-write gap to the smallest window possible without cross-process file locking. * refactor(acp): make per-model catalog probing an injectable resolver Catalog discovery in ACPAgentClient.fetchCatalog ran sequential setSessionConfigOption model switches for every multi-model ACP with a thought_level picker, so a slow or nonconforming agent could stall any provider's catalog probe under the shared 60s timeout. Replace the built-in resolvePerModelThinkingOptions with an optional ACPCatalogModelResolver injected through ACPAgentClientOptions (threaded through GenericACPAgentClient like the existing transformer/parser deps). The base client ships no resolver, so catalog discovery derives models from the initial session response and never switches models. Export the config-option helpers a resolver needs. The base regression test pins that a plain ACPAgentClient with multiple models and a thinking picker never calls setSessionConfigOption. * feat(kimi): resolve per-model thinking options via KimiACPAgentClient Kimi exposes different thinking-effort levels per model but only reports the currently selected model's levels, so the catalog probe must switch through each model to read them back. Move that logic off the shared ACP adapter and onto a Kimi-specific client. Add resolveKimiCatalogModels (the sequential setSessionConfigOption probe, fallback, and warning) and KimiACPAgentClient, which injects it via the new catalogModelResolver dependency. Select it for providerId "kimi" in the registry alongside cursor/kiro/traecli, so only Kimi pays the extra round trips; every other ACP provider skips model switching entirely. Tests cover the four probe behaviors on KimiACPAgentClient and the registry dispatch to it. --------- Co-authored-by: Claude Opus 5 <noreply@anthropic.com>
Paseo
One interface for Claude Code, Codex, Copilot, OpenCode, and Pi agents.
Run agents in parallel on your own machines. Ship from your phone or your desk.
- Self-hosted: Agents run on your machine with your full dev environment. Use your tools, your configs, and your skills.
- Multi-provider: Claude Code, Codex, Copilot, OpenCode, and Pi through the same interface. Pick the right model for each job.
- Voice control: Dictate tasks or talk through problems in voice mode. Hands-free when you need it.
- Cross-device: iOS, Android, desktop, web, and CLI. Start work at your desk, check in from your phone, script it from the terminal.
- Privacy-first: Paseo doesn't have any telemetry, tracking, or forced log-ins.
Getting Started
Paseo runs a local server called the daemon that manages your coding agents. Clients like the desktop app, mobile app, web app, and CLI connect to it.
Prerequisites
You need at least one agent CLI installed and configured with your credentials:
Desktop app (recommended)
Download it from paseo.sh/download or the GitHub releases page. Open the app and the daemon starts automatically. Nothing else to install.
To connect from your phone, open Settings → your host → Pair Device.
CLI / headless
Install the CLI and start Paseo:
npm install -g @getpaseo/cli
paseo
Paseo starts locally, then asks whether to enable the end-to-end encrypted relay for device pairing. If you decline, connect directly over TCP, Tailscale, or another VPN. This path is useful for servers and remote machines.
For full setup and configuration, see:
Docker
Run the Paseo daemon and self-hosted web UI in Docker:
docker run -d --name paseo \
-p 6767:6767 \
-e PASEO_PASSWORD=change-me \
-v "$PWD/paseo-home:/home/paseo" \
-v "$PWD:/workspace" \
ghcr.io/getpaseo/paseo:latest
Open http://localhost:6767 after it starts. Extend the base image with the agent CLIs you use, then provide credentials through environment variables or the persistent /home/paseo volume. See the Docker documentation for full setup details.
CLI
Everything you can do in the app, you can do from the terminal.
paseo run --provider claude/opus-4.6 "implement user authentication"
paseo run --provider codex/gpt-5.4 --worktree feature-x "implement feature X"
paseo ls # list running agents
paseo attach abc123 # stream live output
paseo send abc123 "also add tests" # follow-up task
# run on a remote daemon
paseo --host workstation.local:6767 run "run the full test suite"
See the full CLI reference for more.
Skills
Skills teach your agent to use Paseo to orchestrate other agents.
npx skills add getpaseo/paseo
Then use them in any agent conversation:
/paseo-handoff— hand off work between agents. I use this to plan with Claude and then handoff to Codex to implement./paseo-loop— loop an agent against clear acceptance criteria (aka Ralph loops), optionally with a verifier./paseo-advisor— spin up a single agent as an advisor for a second opinion, without delegating the work itself./paseo-committee— form a committee of two contrasting agents to step back, do root cause analysis, and produce a plan.
Development
Quick monorepo package map:
packages/server: Paseo daemon (agent process orchestration, WebSocket API, MCP server)packages/app: Expo client (iOS, Android, web)packages/cli:paseoCLI for daemon and agent workflowspackages/desktop: Electron desktop apppackages/relay: Relay transport and encryption used by the daemon and clientspackages/website: Marketing site and documentation (paseo.sh)
Common commands:
# run all local dev services
npm run dev
# run individual surfaces
npm run dev:server
npm run dev:app
npm run dev:desktop
npm run dev:website
# build the server stack
npm run build:server
# repo-wide checks
npm run typecheck
Related projects
- getpaseo/paseo-relay — official distributed relay, written in Elixir
- paseo-skins — community themes and a zero-patch desktop theme loader with an Agent Skill
- paseo-vscode — VS Code extension
License
AGPL-3.0